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Division of Work and Fragmented Information: An Explanation for the Diminishing Marginal Product of Labor

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  • Harashima, Taiji

Abstract

In this paper, an explanation for the diminishing marginal product of labor is demonstrated in a model that incorporates the concept of entropy from information theory. First, I introduce the concept of “division of work” and argue that the division of work (i.e., the allocation of tasks in the production process) and not the division of labor (i.e., worker specialization) is the source of the diminishing marginal product of labor. Division of work results in a fragmentation of the information that workers can access, and inefficiencies other than the commonly assumed factors of redundancy and congestion in labor inputs are generated by this fragmentation of information. The introduced inefficiency is modeled using the concept of entropy from information theory and the experience curve effect theory. The mechanism of the diminishing marginal product of labor is well explained by the model, and the model is similarly used to explore the diminishing marginal product of capital.

Suggested Citation

  • Harashima, Taiji, 2014. "Division of Work and Fragmented Information: An Explanation for the Diminishing Marginal Product of Labor," MPRA Paper 56301, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:56301
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    1. Patrick Criqui & Jean-Marie Martin & Leo Schrattenholzer & Tom Kram & Luc Soete & Adriaan Van Zon, 2000. "Energy technology dynamics," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 65-103.
    2. Joskow, Paul L & Rozanski, George A, 1979. "The Effects of Learning by Doing on Nuclear Plant Operating Reliability," The Review of Economics and Statistics, MIT Press, vol. 61(2), pages 161-168, May.
    3. Papineau, Maya, 2006. "An economic perspective on experience curves and dynamic economies in renewable energy technologies," Energy Policy, Elsevier, vol. 34(4), pages 422-432, March.
    4. Miketa, Asami & Schrattenholzer, Leo, 2004. "Experiments with a methodology to model the role of R&D expenditures in energy technology learning processes; first results," Energy Policy, Elsevier, vol. 32(15), pages 1679-1692, October.
    5. Rizavi, Sayyid Salman & Sofer, Catherine, 2009. "Women's Relative Position and the Division of Household Work A Study of French Couples," European Journal of Economic and Social Systems, Lavoisier, vol. 22(2), pages 13-26.
    6. Linda Argote & Bill McEvily & Ray Reagans, 2003. "Managing Knowledge in Organizations: An Integrative Framework and Review of Emerging Themes," Management Science, INFORMS, vol. 49(4), pages 571-582, April.
    7. Dudley, Leonard, 1972. "Learning and Productivity Change in Metal Products," American Economic Review, American Economic Association, vol. 62(4), pages 662-669, September.
    8. Womer, N Keith & Patterson, J Wayne, 1983. "Estimation and Testing of Learning Curves," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 265-272, October.
    9. Martin B. Zimmerman, 1982. "Learning Effects and the Commercialization of New Energy Technologies: The Case of Nuclear Power," Bell Journal of Economics, The RAND Corporation, vol. 13(2), pages 297-310, Autumn.
    10. Olav Sorenson, 2003. "Interdependence and Adaptability: Organizational Learning and the Long--Term Effect of Integration," Management Science, INFORMS, vol. 49(4), pages 446-463, April.
    11. Linda Argote & Sara L. Beckman & Dennis Epple, 1990. "The Persistence and Transfer of Learning in Industrial Settings," Management Science, INFORMS, vol. 36(2), pages 140-154, February.
    12. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    13. Eelke Wiersma, 2007. "Conditions That Shape the Learning Curve: Factors That Increase the Ability and Opportunity to Learn," Management Science, INFORMS, vol. 53(12), pages 1903-1915, December.
    14. Norman Keith Womer, 1984. "Estimating Learning Curves from Aggregate Monthly Data," Management Science, INFORMS, vol. 30(8), pages 982-992, August.
    15. Harashima, Taiji, 2011. "A Model of Total Factor Productivity Built on Hayek’s View of Knowledge: What Really Went Wrong with Socialist Planned Economies?," MPRA Paper 29107, University Library of Munich, Germany.
    16. Marvin B. Lieberman, 1987. "The learning curve, diffusion, and competitive strategy," Strategic Management Journal, Wiley Blackwell, vol. 8(5), pages 441-452, September.
    17. van der Zwaan, Bob & Rabl, Ari, 2004. "The learning potential of photovoltaics: implications for energy policy," Energy Policy, Elsevier, vol. 32(13), pages 1545-1554, September.
    18. Paul S. Adler & Kim B. Clark, 1991. "Behind the Learning Curve: A Sketch of the Learning Process," Management Science, INFORMS, vol. 37(3), pages 267-281, March.
    19. Harashima, Taiji, 2009. "A Theory of Total Factor Productivity and the Convergence Hypothesis: Workers’ Innovations as an Essential Element," MPRA Paper 15508, University Library of Munich, Germany.
    20. Marvin B. Lieberman, 1984. "The Learning Curve and Pricing in the Chemical Processing Industries," RAND Journal of Economics, The RAND Corporation, vol. 15(2), pages 213-228, Summer.
    21. Harashima, Taiji, 2012. "A Theory of Intelligence and Total Factor Productivity: Value Added Reflects the Fruits of Fluid Intelligence," MPRA Paper 43151, University Library of Munich, Germany.
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    More about this item

    Keywords

    Diminishing marginal product; Division of work; Division of labor; The experience curve effect; The quantity of information; Entropy;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • M54 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Labor Management

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